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---
language:
- ko
- en
- es
- pt
tags:
- token-classification
- named-entity-recognition
- multilingual
- transformers
license: mit
pipeline_tag: token-classification
datasets:
- wikiann
model-index:
- name: kaidol-ner-multilingual
results:
- task:
name: Named Entity Recognition
type: token-classification
dataset:
name: WikiAnn (en, ko, es, pt)
type: wikiann
metrics:
- name: F1
type: f1
value: 0.74
base_model:
- Davlan/xlm-roberta-base-ner-hrl
---
# ๐ KAIdol NER Multilingual Model
This is a multilingual NER (Named Entity Recognition) model developed as part of the **KAIdol Project**.
It is based on [`Davlan/xlm-roberta-base-ner-hrl`](https://huggingface.co/Davlan/xlm-roberta-base-ner-hrl), fine-tuned on the [WikiAnn](https://huggingface.co/datasets/wikiann) dataset for **Korean (ko)**, **English (en)**, **Spanish (es)**, and **Portuguese (pt)**.
## ๐ง Model Details
- **Base model**: `Davlan/xlm-roberta-base-ner-hrl`
- **NER Tags**:
- `PER`: Person
- `ORG`: Organization
- `LOC`: Location
- **Tokenizer**: AutoTokenizer from base model
- **Max length**: 128 tokens
## ๐ Training Configuration
| Parameter | Value |
|------------------|-----------|
| Epochs | 5 |
| Batch Size | 16 |
| Optimizer | AdamW |
| Learning Rate | 5e-5 |
| Loss | CrossEntropy with class weights |
| Dataset | WikiAnn (en, ko, es, pt) |
## โ
Performance Summary
| Language | F1-macro | PER F1 | ORG F1 | LOC F1 |
|----------|----------|--------|--------|--------|
| English | 0.74 | 0.84 | 0.63 | 0.76 |
| Korean | 0.43 | 0.46 | 0.30 | 0.52 |
| Spanish | TBD | TBD | TBD | TBD |
| Portuguese | TBD | TBD | TBD | TBD |
> Performance on `es` and `pt` will be updated after evaluation. Korean performance is limited due to tokenization issues in WikiAnn.
## ๐ Usage Example
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("developer-lunark/kaidol-ner-multilingual")
tokenizer = AutoTokenizer.from_pretrained("developer-lunark/kaidol-ner-multilingual")
tokens = tokenizer("Barack Obama naciรณ en Hawรกi.", return_tensors="pt")
output = model(**tokens)
```
## ๐งพ Label Mapping
```python
{
'O': 0,
'B-PER': 1,
'I-PER': 2,
'B-ORG': 3,
'I-ORG': 4,
'B-LOC': 5,
'I-LOC': 6
}
```
## ๐ License
MIT License
## ๐ฌ Contact
Developed by the [KAIdol ํ๋ก์ ํธ ํ].
For questions or collaborations, contact: `developer-lunark` |